Python implementation of some dimensionality reduction methods(PCA, LDA, LLE, NMDS, et al.)
基于python实现一些降维方法，包括算法原理、代码、案例以及参考资料
- PCA(Principal Component Analysis, 主成分分析)
- FLD(Fisher's Linear Discriminant, Fisher线性判别, Fisher)
- LLE(Locally Linear Embedding, 局部线性嵌入)
- LE(Laplacian Eigenmap, 拉普拉斯特征映射)
- PCoA(Principal Coordinates Analysis, 主坐标分析, Classical Multidimensional Scaling, 经典多维尺度分析)
- ISOMAP(Isometric Mapping, 等距映射)
- NMDS(Non-metric multidimensional scaling, 非度量多维尺度分析)
- KPCA(Kernel Principle Component Analysis, 核主成分分析)
- RP(random projection, 随机映射)
- Diffusion maps(Diffusion maps, 扩散映射)
- Auto Encoder-Decoder
- SNE(Stochastic Neighbor Embedding)
- t-SNE(t-distributed stochastic neighbor embedding)
- UMAP(Uniform Manifold Approximation and Projection for Dimension Reduction, 基于一致流形逼近和投影的降维技术)
- LargeVis
和sklearn等库中直接调用函数得到的结果进行对比
